1. Field
This application relates to a method and an apparatus for analyzing data related to a driving range estimation of a vehicle.
2. Description of Related Art
Industry research has revealed that the anxiety felt by many drivers about the remaining driving range of an electric vehicle (EV) before the battery runs down and needs to be charged is a major contributing factor to a low adoption rate of EVs. The anxiety generally occurs since current technology cannot accurately estimate the remaining driving range of an EV.
EV remaining driving range estimation technologies according to the related art mainly rely on the limited collection of data. While some methods put more emphasis on an electrochemical behavior of a battery, there are other methods that focus on identifying different driving patterns. Moreover, some other methods consider more global positioning system (GPS)-based and manufacture-provided data with a simplified EV power train model. In addition, some other methods consider up to nine factors to estimate the driving range of an EV. However, a sensitivity and a reliability of a range estimation algorithm change under different environmental and operational conditions. Accordingly, a structure capable of processing all data related to the driving range estimation may be considered to accurately estimate the driving range of an EV.
In recent times, with an increase in a variety of sensors, wideband communication systems, and cheap memories to observe, measure, and store real-time data related to the driving range of an EV in a vehicle or a cloud, an amount of data collected in the EV is fast increasing. Such large amounts of data may have different levels of accuracy, resolutions, and relevance in unstructured ways. Big data techniques have been emerging to address huge, diverse, and unstructured data to significantly enhance the performance of an entire system.